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RESEARCH PAPER · Advanced Functional Materials · 2021

Applied Machine Learning for Developing Next‐Generation Functional Materials

Filip Dinic, Kamalpreet Singh, Tony Dong, Milad Rezazadeh, Zhibo Wang, Ali Khosrozadeh, Tiange Yuan, Oleksandr Voznyy

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Applied Machine Learning for Developing Next‐Generation Functional MaterialsOriginalAn Interpretable Machine Learning Model to Guide the Design of Organic Phosphorus-Containing Flame Retardants for Improving the Limiting Oxygen Index of Epoxy Resins0Bayesian optimisation with transfer learning for NASICON-type solid electrolytes for all-solid-state Li-metal batteries18Recent advance on machine learning of MXenes for energy storage and conversion45Formula Graph Self‐Attention Network for Representation‐Domain Independent Materials Discovery26Scalable deeper graph neural networks for high-performance materials property prediction111High-Throughput Synthesis of Thin Films for the Discovery of Energy Materials: A Perspective16Data-driven machine learning approach for predicting the capacitance of graphene-based supercapacitor electrodes98Machine Learning Assisted Graphdiyne-Based Nanozyme Discovery55Machine learning-guided design of organic phosphorus-containing flame retardants to improve the limiting oxygen index of epoxy resins76A Rising 2D Star: Novel MBenes with Excellent Performance in Energy Conversion and Storage177How machine learning can accelerate electrocatalysis discovery and optimization125HADB: A materials-property database for hard-coating alloys5Accelerating the development of electrocatalysts for electrochemical nitrogen fixation through theoretical and computational approaches11Strain data augmentation enables machine learning of inorganic crystal geometry optimization1Machine-Learning-Assisted Screening of Interface Passivation Materials for Perovskite Solar Cells107Unconstrained Machine Learning Screening for New Li‐Ion Cathode Materials Enhanced by Class Balancing9Mechanical energy metamaterials in interstellar travel74Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models235Recent advancements on designing transition metal-based carbon-supported single atom catalysts for oxygen electrocatalysis: Miles to go for sustainable Zn-air batteries68Building Smarter Aqueous Batteries23Prospective on methods of design of experiments for limited data scenarios in materials design and engineering6Artificial intelligence-driven rechargeable batteries in multiple fields of development and application towards energy storage34Machine‐Learning Accelerating the Development of Perovskite Photovoltaics12Mechanical metamaterials and beyond733Machine learning models for the discovery of direct band gap materials for light emission and photovoltaics21Machine learning for expediting next-generation of fire-retardant polymer composites43XRDMatch: a semi-supervised learning framework to efficiently discover room temperature lithium superionic conductors12Controlled Synthesis of Space–Time Modulated Metamaterial for Enhanced Nonreciprocity by Machine Learning4Perspectives on Development of Optoelectronic Materials in Artificial Intelligence Age8Electrochemical energy conversion and storage processes with machine learning18Machine learning-based screening of two-dimensional perovskite organic spacers13Knowledge-reused transfer learning for molecular and materials science28Rational Design of Deep Learning Networks Based on a Fusion Strategy for Improved Material Property Predictions12Digital manufacturing of perovskite materials and solar cells12Comparative analysis of rainfall-runoff simulation using a long short-term memory (LSTM) deep learning model and a conceptual hydrological model, HEC-HMS: a case study of the mountainous river basin of Nepal8Accelerated screening for high-efficiency inorganic photovoltaic materials: from ensemble voting regression to device performance1Data-Driven Insights into the Electronic Structure of Il@Pvdf Complexes: A Dft–Ml Approach for High-Performance Gel Polymer Electrolytes0Efficiently screening organic ligands by machine learning for stabilizing 2D/3D perovskites5Enhancing perovskite solar cell efficiency and stability: a multimodal prediction approach integrating microstructure, composition, and processing technology10Machine learning-assisted design and prediction of materials for batteries based on alkali metals8Recent advances in flexible and wearable OLEDs for biomedical applications: a review43Accelerating the design and discovery of tribocorrosion-resistant metals by interfacing multiphysics modeling with machine learning and genetic algorithms16Advanced theoretical modeling methodologies for electrocatalyst design in sustainable energy conversion28Machine learning-assisted structure design and materials screening of a bilayer thin-film selective thermal emitter for infrared stealth technology9Machine Learning Accelerated Discovery of Covalent Organic Frameworks for Environmental and Energy Applications66GCPNet: An interpretable Generic Crystal Pattern graph neural Network for predicting material properties13Rapid prediction of effective absorption bandwidth in PEEK/CF additive manufacturing metastructure via interpretable machine learning56Machine learning perspective: Revealing deep mechanisms and new advances in adsorption and catalysis of gaseous molecules18Machine Learning‐Driven Band Alignment Strategy for Screening 1T‐TMDs‐Based Z‐Scheme Heterostructures toward Efficient Photocatalytic Water Splitting30Industrial applications of layered double hydroxide (LDH) catalysts in high-current density water electrolysis14From Algorithms to Applications: A Comprehensive Review of Machine Learning in Computational Materials Science5Dynamics in electrochemical organic oxidation reactions from in situ and operando techniques48Advancing Flood Forecasting With Wavelet‐ LSTM : The Role of Nonlinearity in Discharge Prediction0Symmetry‐Aware Equivariant Network for Discovering SHG‐Active Materials5Feasibility of high-entropy ceramics as next-generation dry-machining tools1AI-powered OLEDs: speeding up innovation in displays3Designing High-Performance Gel Polymer Electrolytes for Supercapacitors: A Combined DFT and Machine Learning Approach to Ionic Liquid-PVDF Complexes0Learning in higher dimensions: a strategy for alloy electrocatalyst discovery5Machine Learning‐Driven Innovations in Energy and Environmental Catalysis: A Minireview0Modeling of photoemission characteristics for AlGaN nanoarrays photocathode accelerated by machine learning: from aspects of optical properties0Low-velocity impact damage prediction in nano-clay reinforced aluminium honeycomb sandwich panels using machine learning models0Towards a sustainable high-quality materials data ecosystem: frameworks and strategies6AI-guided approaches in organometallic research: cognition in chemistry7AI-powered open-source infrastructure for accelerating materials discovery and advanced manufacturing27AI-driven on-demand design of multifunctional thin-film metamaterials for selective mid-infrared radiation0Machine Learning Prediction of Crystal Structure Stability toward the Design of High-Entropy Oxides2Multi-fidelity learning in materials informatics: methodologies, applications, and outlook2Representation learning of crystal materials using Graph Neural Networks: Passive symmetry challenges and advances0Application of Machine Learning in Predicting the Properties of Two-Dimensional Semiconductor Materials3Machine learning design of electronic packaging materials: A phonon spectrum continuity matching framework for high thermal conductivity0One-month-ahead river discharge forecasting using machine learning and limited hydrometeorological data: the Karatoya-Atrai River basin, Bangladesh0

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